A semi-distributed,physics-based hydrologic model using remotely sensed and Digital Terrain Elevation Data for semi-arid catchments

A practical, hydrologic model (DPHM-RS) is developed for the semi-arid climate of the Canadian Prairies that could adequately account for a river basin's terrain features by sub-dividing it to sub-basins of uneven shapes and sizes (semi-distributed) based on topographic information derived from the digital terrain elevation (DTED) data. Even though computationally modest, DPHM-RS is scientifically vigorous, can effectively assimilate remotely sensed (RS) data, and has most of its parameters determined through RS data and measurements. The hydrologic processes are estimated for each land cover and then aggregated according to percentage of each land cover present within each sub-basin. As evapotranspiration (ET) usually dominates the hydrology of the Canadian Prairies, ET from each land cover is estimated at three levels by the two-source model that separately considers evaporation from soil and plants. The soil moisture at the top active and the transmission zones are estimated by a water budget approach, while the groundwater dynamics by the topographic soil index obtained from DTED. The surface runoff from each sub-basin is routed to the channel network by a kinematic wave response function, and then routed to the basin outlet by the Muskingum-Cunge model. DPHM-RS, is applied to the Paddle River Basin (265?km²) of Central Alberta divided to five sub-basins. It was calibrated with hourly hydroclimatic and RS data collected in the summer of 1996 and validated with data of 1997 and 1998. In both stages, there are good agreements between simulated runoff at the basin outlet with the observed, between simulated surface temperature and net radiation with the observed, between soil moisture and that retrieved from Radarsat-SAR data, and between simulated ET and that estimated by water balance. Encouraging results from these multi-criteria assessments demonstrate the feasibility of semi-distributed, physics-based hydrologic modelling in the dry climate of Canadian Prairies, and the usefulness of RS and DTED data in basin hydrology.     

A simple model to predict soil moisture: Bridging Event and Continuous Hydrological (BEACH) modelling

This paper introduces a simple two-layer soil water balance model developed to Bridge Event And Continuous Hydrological (BEACH) modelling. BEACH is a spatially distributed daily basis hydrological model formulated to predict the initial condition of soil moisture for event-based soil erosion and rainfall–runoff models. The latter models usually require the spatially distributed values of antecedent soil moisture content and other input parameters at the onset of an event. BEACH uses daily meteorological records, soil physical properties, basic crop characteristics and topographical data. The basic processes incorporated in the model are precipitation, infiltration, transpiration, evaporation, lateral flow, vertical flow and plant growth. The principal advantage of this model lies in its ability to provide timely information on the spatially distributed soil moisture content over a given area without the need for repeated field visits. The application of this model to the CATSOP experimental catchment showed that it has the capability to estimate soil moisture content with acceptable accuracy. The root mean squared error of the predicted soil moisture content for 6 monitored locations within the catchment ranged from 0.011 to 0.065 cm³ cm^?3. The predicted daily discharge at the outlet of the study area agreed well with the observed data. The coefficient of determination and Nash–Sutcliffe efficiency of the predicted discharge were 0.824 and 0.786, respectively. BEACH has been developed within freely available GIS and programming language, PCRaster. It is a useful teaching tool for learning about distributed water balance modelling and land use scenario analysis.     

Remote estimation of regional evapotranspiration

Three models are applied to estimating evapotranspiration in central Australia, using limited routine meteorological data and the NOAA-14 AVHRR overpass. By minimizing the difference between model predicted surface temperature and satellite derived temperature to adjust the estimated soil moisture, both an instantaneous physically based model and a one dimensional boundary layer simulation yielded consistent results. This highlights the sensitivity of surface temperature to soil moisture and suggests that radiometric surface temperature can be used to adjust simple water balance estimates of soil moisture providing a simple and effective means of estimating large scale evapotranspiration in remote arid regions.     

Landsat crop identification for watershed water balance determinations

Abstract

An expedient and accurate method for assessing the effect of intensive agriculture on a watershed water balance is needed, particularly for those areas where well irrigation is prevalent or becoming more so. Digital image processing of Landsat-3 data discriminated major field crops and forests in Tift County, Georgia, necessary for a water use assessment. A water balance model based on a modified Penman relationship was developed to estimate evapotranspiration. The remotely sensed data and local meteorological data were then used in a hydrologic model to predict watershed daily water balance. The simulation was evaluated by comparing model performance with water balance measurements for a 15.5 km² instrumented agricultural watershed.     

Temporal and spatial soil moisture change pattern detection in an agricultural area using multi‐temporal Radarsat ScanSAR data

Monitoring the characteristics of spatially and temporally distributed soil moisture is important to the study of hydrology and climatology for understanding and calculating the surface water balance. The major difficulties in retrieving soil moisture with Synthetic Aperture Radar (SAR) measurements are due to the effects of surface roughness and vegetation cover. In this study we demonstrate a technique to estimate the relative soil moisture change by using multi‐temporal C band HH polarized Radarsat ScanSAR data. This technique includes two components. The first is to minimize the effects of surface roughness by using two microwave radar measurements with different incidence angles for estimation of the relative soil moisture change defined as the ratio between two soil volumetric moistures. This was done by the development of a semi‐empirical backscattering model using a database that simulated the Advanced Integral Equation Model for a wide range of soil moisture and surface roughness conditions to characterize the surface roughness effects at different incidence angles. The second is to reduce the effects of vegetation cover on radar measurements by using a semi‐empirical vegetation model and the measurements obtained from the optical sensors (Landsat TM and AVHRR). The vegetation correction was performed based on a first‐order semi‐empirical backscattering vegetation model with the vegetation water content information obtained from the optical sensors as the input. For the validation of this newly developed technique, we compared experimental data obtained from the Southern Great Plain Soil Moisture Experiment in 1997 (SGP97) with our estimations. Comparison with the ground soil moisture measurements showed a good agreement for predication of the relative soil moisture change, in terms of ratio, with a Root Mean Square Error (RMSE) of 1.14. The spatially distributed maps of the relative soil moisture change derived from Radarsat data were also compared with those derived from the airborne passive microwave radiometer ESTAR. The maps of the spatial characteristics of the relative soil moisture change showed comparable results.     

Error identification and decomposition in large stochastic rainfall-runoff models

The quality of the streamflow predictions is known to be dependent on the user's assigned accuracy of the rainfall-runoff model. Based on the maximum likelihood method, a simple procedure is presented to identify the statistics of errors of a conceptual hydrologic model. The procedure is illustrated with data from the Bird Creek river basin in Oklahoma, U.S.A.The problem of forecasting river flows in a basin composed of several interconnected sub-basins is also investigated. Decomposition procedures are proposed to efficiently filter and forecast states in all sub-basins simultaneously. The procedures decouple soil states in different sub-basins and connect them through the river network. This time a branch of the Potomac river, West Virginia, U.S.A., is used as a case study.     

A WebGIS-based system for rainfall-runoff prediction and real-time water resources assessment for Beijing

A WebGIS-based system designed to predict rainfall-runoff and assess real-time water resources for Beijing was developed to provide support for scientific decision making regarding solving water shortages while effectively reducing urban flood threats in the city. The system adopts a Browse Server (B/S) structure and combines the distributed hydrologic modeling and WebGIS techniques. For this system, a distributed hydrologic model of Beijing that adopts a grid cell-size of 1 km by 1 km and covers the city's entire area of 16,400 km² was developed and validated. This model employs a simple, yet practical rainfall-runoff correlation curve method to predict runoff, as well as prediction approaches for rainfall, evaporation, subsurface runoff and recharge to groundwater. In addition, a framework for the assessment of real-time water resources assessment based on hydrologic monitoring stations and the distributed model was established. Finally, a WebGIS-based system for rainfall-runoff prediction and real-time water resources assessment for Beijing was developed by integrating a data platform, the professional models and the WebGIS techniques. This system was successfully integrated into the hydrologic prediction practices of the General Station of Hydrology, Bureau of Beijing Water Affairs in 2005, and the demonstration version of the system can be seen on the Web at http://123.127.143.23/enewRF/login/login.aspx?ReturnUrl=%2fenewRF%2ftemp.aspx.     

Application of a distributed large basin runoff model in the Great Lakes basin

This paper analyzes the application of a spatially distributed large basin runoff model (DLBRM) in the Great Lakes Basin of the United Stats and Canada and discusses four essential components of operational hydrologic model development: model structure, model input, model calibration, and Geographical Information System (GIS)-model interface. The results indicate that large scale operational hydrologic models that are based on mass continuity equations and include land surface, soil zones, and groundwater components require fewer parameters, are less data demanding, and are particularly suitable for solving water resources problems over large spatial and temporal scales than many other models. Use of GIS-model interfaces is essential for utilizing the existing multiple digital databases in defining model input and in facilitating model implementation and applicability.     

An efficient integrated approach for global sensitivity analysis of hydrological model parameters

Efficient sensitivity analysis, particularly for the global sensitivity analysis (GSA) to identify the most important or sensitive parameters, is crucial for understanding complex hydrological models, e.g., distributed hydrological models. In this paper, we propose an efficient integrated approach that integrates a qualitative screening method (the Morris method) with a quantitative analysis method based on the statistical emulator (variance-based method with the response surface method, named the RSMSobol' method) to reduce the computational burden of GSA for time-consuming models. Using the Huaihe River Basin of China as a case study, the proposed approach is used to analyze the parameter sensitivity of distributed time-variant gain model (DTVGM). First, the Morris screening method is used to qualitatively identify the parameter sensitivity. Subsequently, the statistical emulator using the multivariate adaptive regression spline (MARS) method is chosen as an appropriate surrogate model to quantify the sensitivity indices of the DTVGM. The results reveal that the soil moisture parameter WM is the most sensitive of all the responses of interest. The parameters Kaw and g₁ are relatively important for the water balance coefficient (WB) and Nash–Sutcliffe coefficient (NS), while the routing parameter RoughRss is very sensitive for the Nash–Sutcliffe coefficient (NS) and correlation coefficient (RC) response of interest. The results also demonstrate that the proposed approach is much faster than the brute-force approach and is an effective and efficient method due to its low CPU cost and adequate degree of accuracy.     

A thermal-based remote sensing technique for routine mapping of land-surface carbon,water and energy fluxes from field to regional scales

Robust yet simple remote sensing methodologies for mapping instantaneous land-surface fluxes of water, energy and CO₂ exchange within a coupled framework add significant value to large-scale monitoring networks like FLUXNET, facilitating upscaling of tower flux observations to address questions of regional carbon cycling and water availability. This study investigates the implementation of an analytical, light-use efficiency (LUE) based model of canopy resistance within a Two-Source Energy Balance (TSEB) scheme driven primarily by thermal remote sensing inputs. The LUE model computes coupled canopy-scale carbon assimilation and transpiration fluxes, and replaces a Priestley–Taylor (PT) based transpiration estimate used in the original form of the TSEB model. In turn, the thermal remote sensing data provide valuable diagnostic information about the sub-surface moisture status, obviating the need for precipitation input data and prognostic modeling of the soil water balance. Both the LUE and PT forms of the model are compared with eddy covariance tower measurements acquired in rangeland near El Reno, OK. The LUE method resulted in improved partitioning of the surface energy budget, capturing effects of midday stomatal closure in response to increased vapor pressure deficit and reducing errors in half-hourly flux predictions from 16 to 12%. The spatial distribution of CO₂ flux was mapped over the El Reno study area using data from an airborne thermal imaging system and compared to fluxes measured by an aircraft flying a transect over rangeland, riparian areas, and harvested winter wheat. Soil respiration contributions to the net carbon flux were modeled spatially using remotely sensed estimates of soil surface temperature, soil moisture, and leaf area index. Modeled carbon and water fluxes from this heterogeneous landscape compared well in magnitude and spatial pattern to the aircraft fluxes. The thermal inputs proved to be valuable in modifying the effective LUE from a nominal species-dependent value. The model associates cooler canopy temperatures with enhanced transpiration, indicating higher canopy conductance and carbon assimilation rates. The surface energy balance constraint in this modeling approach provides a useful and physically intuitive mechanism for incorporating subtle signatures of soil moisture deficiencies and reduced stomatal aperture, manifest in the thermal band signal, into the coupled carbon and water flux estimates.     

Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model

Two types of Soil Vegetation Atmosphere Transfer (SVAT) modeling approaches can be applied to monitor root-zone soil moisture in agricultural landscapes. Water and Energy Balance (WEB) SVAT modeling is based on forcing a prognostic root-zone water balance model with observed rainfall and predicted evapotranspiration. In contrast, thermal Remote Sensing (RS) observations of surface radiometric temperature (T_R) are integrated into purely diagnostic RS-SVAT models to predict the onset of vegetation water stress. While RS-SVAT models do not explicitly monitor soil moisture, they can be used in the calculation of thermal-based proxy variables for the availability of soil water in the root zone. Using four growing seasons (2001 to 2004) of profile soil moisture, micro-meteorology, and surface radiometric temperature measurements at the United States Department of Agriculture (USDA) Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) study site in Beltsville, MD, prospects for improving WEB-SVAT root-zone soil water predictions via the assimilation of diagnostic RS-SVAT soil moisture proxy information are examined. Results illustrate the potential advantages of such an assimilation approach relative to the competing approach of directly assimilating T_R measurements. Since T_R measurements used in the analysis are tower-based (and not obtained from a remote platform), a sensitivity analysis demonstrates the potential impact of remote sensing limitations on the value of the RS-SVAT proxy. Overall, results support a potential role for RS-SVAT modeling strategies in improving WEB-SVAT model characterization of root-zone soil moisture.     

Physical-based soil moisture retrieval method over bare agricultural areas by means of multi-sensor SAR data

The main objective of this research is to develop, test and validate soil moisture retrieval method based on multi-source SAR (Synthetic Aperture Radar) data for bare agricultural areas. The Radardat-2, TerraSAR-X and Sentinel-1A SAR data were applied to retrieve soil moisture content in combination with the integral equation model (IEM) or calibrated integral equation model (CIEM). A straightforward inversion scheme was developed, which does not require the prior knowledge of surface roughness. The soil moisture content can be directly estimated using a look-up table (LUT) optimization method with multi-source SAR data as inputs. For validation purpose, in situ soil moisture content was measured during the period of SAR data acquisitions. The effectiveness and reliability of the soil moisture retrieval methods were evaluated based on the in situ measurements and cost function distribution graph. The experimental results indicate that the developed approach provided accurate soil moisture estimates with root mean square errors (RMSE) ranging from 0.047 cm³ cm^?3 to 0.079 cm³ cm^?3 over the experimental areas. The distribution graphs of the cost function demonstrate the uniqueness and convergence of the estimated results based on multi-source SAR data. Either IEM or CIEM was employed to estimate soil moisture content, more accurate results were obtained with Radarsat-2, TerraSAR-X and Sentinel-1A data as inputs. The experimental results preliminary illustrate that the multi-source SAR data are promising for soil moisture retrieval over bare agricultural areas. The novelty of the presented research can be summarized as two aspects. Firstly, the multi-sensor SAR with different incidence angle, different frequency and different polarization were combined to estimate soil moisture content by means of the physical-based methods. The combination of the multi-sensor SAR data can effectively solve the ill-posed problem of soil moisture retrieval using physical models. Secondly, the CIEM was utilized to establish the soil moisture retrieval model, which transforms the three unknown parameters to two unknown parameters. Furthermore, the convergence and uniqueness of the estimated soil moisture were validated through distribution graphs of the cost function.     

Evaluation of a static water balance model in cropped and grazed systems of temperate Australia

Several water balance models have been developed for Australian conditions, however few of them were developed for the mixed cropping and grazing systems that are typical of temperate south-east Australia. HowLeaky? is a 1-dimensional water balance model that allows simulation of both cropped and grazed systems. This study tested the accuracy of HowLeaky? simulations of soil moisture content and surplus water (runoff plus deep drainage) for mixed farming systems in south-east Australia. Two datasets from the state of Victoria were used to validate model simulations: 1) Rutherglen, consisting of four years soil moisture observations and deep drainage estimates from seven treatments of crop rotations and annual pasture; 2) Vasey, consisting of three years soil moisture and runoff observations and deep drainage estimates from a perennial pasture. A static plant growth option was applied to simulate seasonal crop and pasture covers. Despite the static approach not accounting for inter-annual variation in crop development, HowLeaky? captured observed soil moisture trends, with the Nash Sutcliffe efficiency ranging from fair (0.34) in continuous crop, moderate (0.64–0.68) in annual and perennial pasture, to very good (>0.70) for continuous lucerne and lucerne-crop rotations. Annual water surplus was generally underestimated in this uncalibrated application of the model suggesting a need for some degree of model calibration for highly uncertain and influential parameters such as field capacity.     

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Soil moisture is an important parameter that influences the exchange of water and energy fluxes between the land surface and the atmosphere. Through the simulation by a Soil–Vegetation–Atmosphere Transfer model, Carlson proposed the universal spatial information-based method to determine soil moisture that is insensitive to the initial atmospheric and surface conditions, net radiation, and atmospheric correction. In this study, a practical normalized soil moisture model is established to describe the relationship among the normalized soil moisture (M), the normalized land surface temperature (T*), and the fractional vegetation cover. The dry and wet points are determined using the surface energy balance principle, which has a robust physical basis. This method is applied to retrieve soil moisture for the Soil Moisture-Atmosphere Coupling Experiment campaign in the Walnut Creek watershed, which has a humid climate, and at the Linzestation, which has a semi-arid climate. The validation data are obtained on days of year (DOYs) 182 and 189 in 2002 in the humid region and on DOYs 148 and 180 in 2008 for the semi-arid region; these data collection days are coincident with the overpass of the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus. When the estimates are compared with the in situ measurements of soil water content, the root mean square error is approximately 0.10 m³ m^?3 with a bias of 0.05 m³ m^?3 for the humid region and 0.08 m³ m^?3 with a bias of 0.03 m³ m^?3 for the semi-arid region. These results demonstrate that the practical normalized soil moisture model is applicable in both humid and semi-arid regions.     

Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04

Soil moisture status in the root zone is an important component of the water cycle at all spatial scales (e.g., point, field, catchment, watershed, and region). In this study, the spatio-temporal evolution of root zone soil moisture of the Walnut Gulch Experimental Watershed (WGEW) in Arizona was investigated during the Soil Moisture Experiment 2004 (SMEX04). Root zone soil moisture was estimated via assimilation of aircraft-based remotely sensed surface soil moisture into a distributed Soil-Water-Atmosphere-Plant (SWAP) model. An ensemble square root filter (EnSRF) based on a Kalman filtering scheme was used for assimilating the aircraft-based soil moisture observations at a spatial resolution of 800 m × 800 m. The SWAP model inputs were derived from the SSURGO soil database, LAI (Leaf Area Index) data from SMEX04 database, and data from meteorological stations/rain gauges at the WGEW. Model predictions are presented in terms of temporal evolution of soil moisture probability density function at various depths across the WGEW. The assimilation of the remotely sensed surface soil moisture observations had limited influence on the profile soil moisture. More specifically, root zone soil moisture depended mostly on the soil type. Modeled soil moisture profile estimates were compared to field measurements made periodically during the experiment at the ground based soil moisture stations in the watershed. Comparisons showed that the ground-based soil moisture observations at various depths were within ± 1 standard deviation of the modeled profile soil moisture. Density plots of root zone soil moisture at various depths in the WGEW exhibited multi-modal variations due to the uneven distribution of precipitation and the heterogeneity of soil types and soil layers across the watershed.     

Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests

Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (potential crop yield). Hence, the detection of soil moisture content (SMC) is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. A method to estimate SMC from optical and thermal spectral information of METEOSAT imagery based on thermal inertia (TI) is presented. Minimum and maximum TI values from time series are combined in the Soil Moisture Saturation Index (SMSI). To convert surface to soil profile values, a Markov type filter is used, based on a simple two layer water balance equation (the surface layer and the reservoir below) and an autocorrelation function. Ten-daily SMC values are compared with up-scaled (using AVHRR/NDVI) observations on 10 EUROFLUX sites in Europe for the 1997 growing season (March-October). Moreover, the thermal inertia approach is compared for 1997, with ERS Scatterometer data for eight EUROFLUX sites. METEOSAT pixels are up-scaled to accommodate the ERS Scatterometer spatial resolution. The regression coefficients (slope, intercept and R²) of the thermal inertia approach versus the up-scaled soil moisture observations from EUROFLUX sites vary between 0.811-1.148, − 0.0029-0.66 and 0.544-0.877, respectively, with a RRMSE range of 3.9% to 35.7%. The regression coefficients of the comparison of ERS Scatterometer derived Soil Water Index (SWI) versus the up-scaled Soil Moisture Saturation Index for the pooled case (binning the eight EUROFLUX sites) are 0.587, 0.105 and 0.441, respectively, with a RRMSE of 38%. A simple error propagation model applied for the thermal inertia approach reveals that the absolute and relative errors of the obtained soil moisture content is at least 0.010 m³ m^− 3 or 2.0% with a SMC of 0.203 m³ m^− 3. Recommendations are made to test and implement the TI methodology using NOAA/AVHRR imagery.     

On Latin Hypercube sampling for efficient uncertainty estimation of satellite rainfall observations in flood prediction

With the advent of the Global Precipitation Measurement (GPM) in 2009, satellite rainfall measurements are expected to become globally available at space–time scales relevant for flood prediction of un-gauged watersheds. For uncertainty assessment of such retrievals in flood prediction, error models need to be developed that can characterize the satellite's retrieval error structure. A full-scale assessment would require a large number of Monte Carlo (MC) runs of the satellite error model realizations, each passed through a hydrologic model, in order to derive the probability distribution in runoff. However, for slow running hydrologic models this can be computationally expensive and sometimes prohibitive. In this study, Latin Hypercube Sampling (LHS) was implemented in a satellite rainfall error model to explore the degree of computational efficiency that could be achieved with a complex hydrologic model. It was found that the LHS method is particularly suited for storms with moderate rainfall. For assessment of errors in time to peak, peak runoff, and runoff volume no significant computational advantage of LHS over the MC method was observed. However, the LHS was able to produce the 80% and higher confidence limits in runoff simulation with the same degree of reliability as MC, but with almost two orders of magnitude fewer simulations. Results from this study indicate that a LHS constrained sampling scheme has the potential to achieve computational efficiency for hydrologic assessment of satellite rainfall retrievals involving: (1) slow running models (such as distributed hydrologic models and land surface models); (2) large study regions; and (3) long study periods; provided the assessment is confined to analysis of the large error bounds of the runoff distribution.     

Investigation of effect of heterogeneities in vegetation and rainfall on simulated SSM/I brightness temperatures

Land surface characteristics: soil and vegetation and rainfall inputs are distributed in nature. Representation of land surface characteristics and inputs in models is lumped at spatial scales corresponding to the grid size or observation density. Complete distributed representation of these characteristics or inputs is infeasible due to excessive computational costs or costs associated with maintaining dense observational networks. The measurements of microwave brightness temperatures by the SSM/I (Special Sensor Microwave Imager) are at resolutions of the order of 56km 56km for 19 GHz and 33 km 33 km for 37 GHz. At these resolutions, soil moisture and vegetation are not homogeneous over the measurement area. The experiments carried out in this study determine the effect of heterogeneities in vegetation (leaf area index) and input rainfall on simulated soil moisture and brightness temperatures and the inversion of brightness temperatures to obtain soil moisture estimates. This study would help us to understand the implications of using the SSM/I microwave brightness temperatures for soil moisture estimation. The consequences of treating rainfall inputs and vegetation over large land surface areas in a lumped fashion is examined. Simpler methods based on dividing the leaf area index or input rainfall into classes rather than explicit representation for representing heterogeneities in leaf area index and spatial distribution of rainfall is tested. It is seen that soil moisture is affected by the representation (lumped vs distributed) of rainfall and not leaf area index. The effect of spatially distributed soil moisture on the inversion of observed SSM/I brightness temperatures to obtain soil moisture estimates is investigated. The inversion process does not exhibit biases in the retrieval of soil moisture. The methodology presented in this paper can be used for any satellite sensor for purposes of analysis and evaluation.     

A software tool for translating deterministic model results into probabilistic assessments of water quality standard compliance

Most assessments of whether a water body will comply with pollutant standards after modification of land use, loading, or climate change are based on the results of deterministic simulation models. These models, including those used to support the United States Environmental Protection Agency (USEPA) total maximum daily load (TMDL) program, typically do not account for common sources of assessment uncertainty. Instead, model results are typically represented by a time series of predicted pollutant concentration values or the parameters of a frequency-based distribution of these values over a specified time period. The rate of exceedance of relevant pollutant limits is then assessed directly from this time series or distribution to determine standard compliance. In this way, sampling and analysis-based variability and model uncertainty are typically ignored, although they may substantially influence the probability of non-compliance. To help address this problem, we introduce ProVAsT (Probabilistic Water Quality Standard Violation Assessment Tool), a software tool encoded in the graphical model-based package Analytica^®. Here, we present a version of ProVAsT which translates model-predicted in situ fecal indicator bacteria (FIB) pollutant concentrations into the expected frequency of water quality standard violations and provides a Bayesian measure of the degree of confidence in this assessment. We call this version ProVAsT-FIB. Along with inputting their own simulation model results, users can specify the particular water quality analysis methods employed (e.g. the analytic procedure used and the number and volume of sample aliquots) as well as the numeric limits pertaining to local water quality standards. It is our hope that ProVAsT will encourage the rational consideration of uncertainty and variability in water quality assessments by reducing the burden of complex statistical calculations.     

Changes in the soil erosion status in the middle and lower reaches of the Yangtze River basin from 2001 to 2014 and the impacts of erosion on the water quality of lakes and reservoirs

ABSTRACT

The revised universal soil loss equation (RUSLE) was used to obtain the soil erosion intensity distribution in the middle and lower reaches of the Yangtze River basin (MLYB), where the input data included a digital elevation model (DEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing products. Changes in the soil erosion intensity throughout the MLYB were analysed from 2001 to 2014, and the potential influences of these changes on the local water quality of lakes and reservoirs were revealed. This investigation is the first to reveal the spatial and temporal changes in soil erosion throughout the MLYB. The results indicated that from 2001 to 2014, most of the MLYB was characterized by slight and light soil erosion levels, whereas relatively few areas exhibited intensive to severe soil erosion. Soil erosion in the MLYB displayed a decreasing trend from 2001 to 2014; over 80% of the region displayed a decreasing soil erosion intensity change rate, indicating that soil conservation in most of the MLYB has improved over the past 14 years. However, 12.8% of the area presented an increasing change rate, and the region with the maximum increasing change rate was located mainly in the lower Yangtze basin. Furthermore, spatial heterogeneities were found in the soil erosion intensities throughout the MLYB: soil erosion improved in the upper and middle regions of the MLYB, whereas soil erosion worsened in the lower regions of the MLYB. Among the sub-basins of the MLYB, obvious soil erosion occurred most frequently in the Hanjiang basin and least frequently in the Taihu basin. A driving force analysis showed that the influence of precipitation on soil erosion is more evident than that of human activities in all sub-basins except the Dongting basin. A correlation analysis between soil erosion and water turbidity/water transparency showed that 45.9% of the decreasing water turbidity is correlated with decreasing soil erosion and that 42.5% of the increasing water turbidity might be influenced by increasing soil erosion. Decreased soil erosion might be responsible for the improved water transparency for 50% of the lakes, whereas increased soil erosion is correlated with a decrease in water transparency for over 50% of the lakes.